Another line of algorithms is to compensate for the insufficient number of available samples by generation. Most methods use the idea of Generative Adversarial Networks (GANs) [1]} or autoencoder  [2]} to generate samples ([3]}, [4]}, [5]}, [6]}) or features ([7]}, [8]}) to augment the training set.
Specifically, [3]} and [7]} proposed to synthesize data by introducing an adversarial generator conditioned on tasks.
[8]} tried to learn a variational autoencoder to approximate the distribution and predict labels based on the estimated statistics.
The autoencoder can also augment samples by projecting between the visual space and the semantic space [4]} or encoding the intra-class deformations [5]}.
[14]} and [15]} propose to generate features through the class hierarchy.
While these methods can generate extra samples or features for training, they require the design of a complex model and loss function to learn how to generate. However, our distribution calibration strategy is simple and does not need extra learnable parameters.
